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    Quantized Feature Index Trajectory
    32.
    发明申请
    Quantized Feature Index Trajectory 失效
    量化特征索引轨迹

    公开(公告)号：US20090043575A1

    公开(公告)日：2009-02-12

    申请号：US11835389

    申请日：2007-08-07

    Applicant: R. Donald Thompson ,  Kunal Mukerjee

    Inventor： R. Donald Thompson ,  Kunal Mukerjee

    IPC: G10L19/00

    CPC classification number: G10L15/02 ,  G10L19/0018 ,  G10L2015/025

    Abstract: Indexing methods are described that may be used by databases, search engines, query and retrieval systems, context sensitive data mining, context mapping, language identification, image recognition, and robotic systems. Raw baseline features from an input signal are aggregated, abstracted and indexed for later retrieval or manipulation. The feature index is the quantization number for the underlying features that are represented by an abstraction. Trajectories are used to signify how the features evolve over time. Features indexes are linked in an ordered sequence indicative of time quanta, where the sequence represents the underlying input signal. An example indexing system based on the described processes is an inverted index that creates a mapping from features or atoms to the underlying documents, files, or data. A highly optimized set of operations can be used to manipulate the quantized feature indexes, where the operations can be fine tuned independent from the base feature set.

    Abstract translation: 描述了可由数据库，搜索引擎，查询和检索系统，上下文相关数据挖掘，上下文映射，语言识别，图像识别和机器人系统使用的索引方法。 来自输入信号的原始基线特征被聚合，抽象和索引，以供以后检索或操纵。 特征索引是由抽象表示的底层特征的量化数。 轨迹用于表示随着时间的推移，特征如何演变。 特征索引以指示时间量子的有序序列链接，其中序列表示底层输入信号。 基于所描述的过程的示例索引系统是反向索引，其创建从特征或原子到底层文档，文件或数据的映射。 可以使用高度优化的操作集来操纵量化的特征索引，其中可以独立于基本特征集来微调操作。

    Uncertainty interval content sensing
    33.
    发明申请
    Uncertainty interval content sensing 失效
    不确定性间隔内容检测

    公开(公告)号：US20070288231A1

    公开(公告)日：2007-12-13

    申请号：US11449354

    申请日：2006-06-08

    Applicant: Kunal Mukerjee ,  Rafael Ballesteros

    Inventor： Kunal Mukerjee ,  Rafael Ballesteros

    IPC: G06F17/27

    CPC classification number: G06Q30/02

    Abstract: Repetition of content words in a communication is used to increase the certainty, or, alternatively, reduce the uncertainty, that the content words were actual words from the communication. Reducing the uncertainty of a particular content word of a communication in turn increases the likelihood that the content word is relevant to the communication. Reliable, relevant content words mined from a communication can be used for, e.g., automatic internet searches for documents and/or web sites pertinent to the communication. Reliable, relevant content words mined from a communication can also, or alternatively, be used to automatically generate one or more documents from the communication, e.g., communication summaries, communication outlines, etc.

    Abstract translation: 通信中的内容词的重复用于增加确定性，或者替代地减少不确定性，内容词是来自通信的实际单词。 降低通信的特定内容词的不确定性反过来增加了内容词与通信相关的可能性。 从通信挖掘的可靠的相关内容词可用于例如与通信相关的文档和/或网站的自动互联网搜索。 从通信中挖掘的可靠的，相关的内容词也可以或者替代地用于从通信中自动生成一个或多个文档，例如通信摘要，通信大纲等。

    Coding of motion vector information
    36.
    发明申请
    Coding of motion vector information 审中-公开
    运动矢量信息编码

    公开(公告)号：US20050013498A1

    公开(公告)日：2005-01-20

    申请号：US10622841

    申请日：2003-07-18

    Applicant: Sridhar Srinivasan ,  Pohsiang Hsu ,  Thomas Holcomb ,  Kunal Mukerjee ,  Bruce Lin

    Inventor： Sridhar Srinivasan ,  Pohsiang Hsu ,  Thomas Holcomb ,  Kunal Mukerjee ,  Bruce Lin

    IPC: G06K9/36 ,  G06K9/46

    CPC classification number: H04N19/137 ,  G06K9/36 ,  G06K9/46 ,  H04N7/52 ,  H04N19/132 ,  H04N19/139 ,  H04N19/176 ,  H04N19/51 ,  H04N19/513 ,  H04N19/61 ,  H04N19/63 ,  H04N19/91

    Abstract: Techniques and tools for encoding and decoding motion vector information for video images are described. For example, a video encoder yields an extended motion vector code by jointly coding, for a set of pixels, a switch code, motion vector information, and a terminal symbol indicating whether subsequent data is encoded for the set of pixels. In another aspect, an encoder/decoder selects motion vector predictors for macroblocks. In another aspect, a video encoder/decoder uses hybrid motion vector prediction. In another aspect, a video encoder/decoder signals a motion vector mode for a predicted image. In another aspect, a video decoder decodes a set of pixels by receiving an extended motion vector code, which reflects joint encoding of motion information together with intra/inter-coding information and a terminal symbol. The decoder determines whether subsequent data exists for the set of pixels based on e.g., the terminal symbol.

    Abstract translation: 描述用于编码和解码用于视频图像的运动矢量信息的技术和工具。 例如，视频编码器通过针对一组像素共同编码一个开关码，运动矢量信息和指示后续数据是否被编码用于像素集合的终端符号来产生扩展运动矢量码。 在另一方面，编码器/解码器为宏块选择运动矢量预测器。 在另一方面，视频编码器/解码器使用混合运动矢量预测。 在另一方面，视频编码器/解码器针对预测图像发送运动矢量模式。 在另一方面，视频解码器通过接收扩展运动矢量码来解码一组像素，该扩展运动矢量码反映运动信息的联合编码以及帧内/帧间编码信息和终端符号。 解码器基于例如终端符号确定对于像素集合是否存在后续数据。

    Coding of motion vector information
    37.
    发明授权
    Coding of motion vector information 有权
    运动矢量信息编码

    公开(公告)号：US08917768B2

    公开(公告)日：2014-12-23

    申请号：US12275782

    申请日：2008-11-21

    Applicant: Sridhar Srinivasan ,  Pohsiang Hsu ,  Thomas W. Holcomb ,  Kunal Mukerjee ,  Bruce Chih-Lung Lin

    Inventor： Sridhar Srinivasan ,  Pohsiang Hsu ,  Thomas W. Holcomb ,  Kunal Mukerjee ,  Bruce Chih-Lung Lin

    IPC: H04N7/12 ,  H04N11/02 ,  H04N11/04 ,  H04N19/51 ,  H04N19/61 ,  G06K9/36 ,  G06K9/46 ,  H04N19/583 ,  H04N19/91

    CPC classification number: H04N19/137 ,  G06K9/36 ,  G06K9/46 ,  H04N7/52 ,  H04N19/132 ,  H04N19/139 ,  H04N19/176 ,  H04N19/51 ,  H04N19/513 ,  H04N19/61 ,  H04N19/63 ,  H04N19/91

    Abstract: Techniques and tools for encoding and decoding motion vector information for video images are described. For example, a video encoder yields an extended motion vector code by jointly coding, for a set of pixels, a switch code, motion vector information, and a terminal symbol indicating whether subsequent data is encoded for the set of pixels. In another aspect, an encoder/decoder selects motion vector predictors for macroblocks. In another aspect, a video encoder/decoder uses hybrid motion vector prediction. In another aspect, a video encoder/decoder signals a motion vector mode for a predicted image. In another aspect, a video decoder decodes a set of pixels by receiving an extended motion vector code, which reflects joint encoding of motion information together with intra/inter-coding information and a terminal symbol. The decoder determines whether subsequent data exists for the set of pixels based on e.g., the terminal symbol.

    Abstract translation: 描述用于编码和解码用于视频图像的运动矢量信息的技术和工具。 例如，视频编码器通过针对一组像素共同编码一个开关码，运动矢量信息和指示后续数据是否被编码用于像素集合的终端符号来产生扩展运动矢量码。 在另一方面，编码器/解码器为宏块选择运动矢量预测器。 在另一方面，视频编码器/解码器使用混合运动矢量预测。 在另一方面，视频编码器/解码器针对预测图像发送运动矢量模式。 在另一方面，视频解码器通过接收扩展运动矢量码来解码一组像素，该扩展运动矢量码反映运动信息的联合编码以及帧内/帧间编码信息和终端符号。 解码器基于例如终端符号确定对于像素集合是否存在后续数据。

    Noise robust speech classifier ensemble
    39.
    发明授权
    Noise robust speech classifier ensemble 有权
    噪声鲁棒的语音分类器集合

    公开(公告)号：US08412525B2

    公开(公告)日：2013-04-02

    申请号：US12433143

    申请日：2009-04-30

    Applicant: Kunal Mukerjee ,  Kazuhito Koishida ,  Shankar Regunathan

    Inventor： Kunal Mukerjee ,  Kazuhito Koishida ,  Shankar Regunathan

    IPC: G10L15/04 ,  G10L15/20 ,  G06F17/30

    CPC classification number: G10L15/142 ,  G10L15/16 ,  G10L15/197 ,  G10L15/20 ,  G10L21/0208 ,  G10L21/0216 ,  G10L25/18 ,  G10L25/93

    Abstract: Embodiments for implementing a speech recognition system that includes a speech classifier ensemble are disclosed. In accordance with one embodiment, the speech recognition system includes a classifier ensemble to convert feature vectors that represent a speech vector into log probability sets. The classifier ensemble includes a plurality of classifiers. The speech recognition system includes a decoder ensemble to transform the log probability sets into output symbol sequences. The speech recognition system further includes a query component to retrieve one or more speech utterances from a speech database using the output symbol sequences.

    Abstract translation: 公开了实现包括语音分类器集合的语音识别系统的实施例。 根据一个实施例，语音识别系统包括将表示语音向量的特征向量转换为对数概率集的分类器集合。 分类器集合包括多个分类器。 语音识别系统包括将对数概率集合变换为输出符号序列的解码器集合。 该语音识别系统还包括一个查询组件，用于使用输出符号序列从语音数据库中检索一个或多个语音话语。

    Word clustering for input data
    40.
    发明授权
    Word clustering for input data 有权
    用于输入数据的Word聚类

    公开(公告)号：US08249871B2

    公开(公告)日：2012-08-21

    申请号：US11283149

    申请日：2005-11-18

    Applicant: Kunal Mukerjee

    Inventor： Kunal Mukerjee

    IPC: G06F17/28 ,  G06F17/27 ,  G06F17/21 ,  G10L15/00 ,  G10L15/14 ,  G10L15/26 ,  G10L17/00 ,  G10L15/04

    CPC classification number: G10L15/063 ,  G10L15/183 ,  G10L15/19 ,  G10L2015/0631

    Abstract: A clustering tool to generate word clusters. In embodiments described, the clustering tool includes a clustering component that generates word clusters for words or word combinations in input data. In illustrated embodiments, the word clusters are used to modify or update a grammar for a closed vocabulary speech recognition application.

    Abstract translation: 用于生成单词簇的聚类工具。 在所描述的实施例中，聚类工具包括为输入数据中的单词或单词组合生成单词簇的聚类组件。 在所示实施例中，单词群集用于修改或更新封闭词汇语音识别应用程序的语法。

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